Neurotechnology video analytics to be deployed at UAE free trade zone

Neurotechnology has landed a deal to deploy its video analytics surveillance across one of the United Arab Emirates’s largest free trade zones, the Ras Al Khaimah Economic Zone (RAKEZ).

The Lithuania-headquartered software developer will help monitor vehicle and pedestrian flow across the 33 million square meter (8,100 acres) trade zone through its facial, vehicle and license plate recognition technology.

The software will be deployed in RAKEZ by IT company COMSTAR Security, which will integrate it into its V.A.S.T. (Validated Access and Secure Tracking) system. Aside from recognition systems and access control, the platform includes an automated weighbridge system, a technology used to measure the weight of a vehicle and its cargo.

With over 35,000 companies registered, the free zone sees high traffic and cargo movement, which requires a complex security and logistics framework, the two companies explain in a release.

Neurotechnology’s video analytics system, called SentiVeillance Cluster, will consolidate up to 260 surveillance cameras across the free trade zone into a unified system. The platform will allow registered vehicles and individuals to gain access to the site.

The SentiVeillance technology identifies the vehicle type, make and model along with Automatic License Plate Recognition (ALPR). Its face algorithms allow for “near real-time” identification and verification of individuals.

Aside from access control, the platform also offers a face and license plate watchlist and a search function which allows users to search for people according to features such as gender, age, facial hair, eyeglasses and others. Vehicles can also be searched according to color, type and direction.

“We are proud that COMSTAR chose our technology as a key security component in a system designed to manage such a complex and busy environment like RAKEZ,” says Vytautas Pranckėnas, SentiVeillance product lead.

Neurotechnology presents latest NIST results for fingerprint biometrics

Neurotechnology has published the results of its latest biometric identification evaluation performed by the National Institute of Standards and Technology (NIST) related to its MegaMatcher Criminal Investigation product. The system allows forensic and law enforcement to identify individuals from latent prints.

NIST’s Evaluation of Latent Friction Ridge Technology (ELFT) assesses how well biometric algorithms can accurately identify a person from latent fingerprints found at crime scenes. The test relies on large-scale datasets provided by the U.S. government and law enforcement institutions, including the Department of Defense (DoD) and the FBI.

Neurotechnology’s algorithm ranked third in the DoD dataset, fourth in the FBI Laboratory Solved dataset and shared first place in the FBI Laboratory dataset, the company announced last month.

“The evaluation covered three datasets: the DoD Dataset #1 with 5,259 searches, the FBI Laboratory Solved #1 dataset with 516 searches and the FBI Laboratory dataset with 49 searches,” the firm wrote in its release.

ELFT relies on two main testing metrics, including Identification (FNIR@FPIR) and Investigation (FNIR@Rank_X).

The former uses a fixed threshold to determine a match without human intervention to measure a False Negative Identification Rate (FNIR) at a specific tolerance for false positives (FPIR). The latter provides a ranked list of potential matches and measures the biometric algorithm’s accuracy (FNIR) at a given rank, indicating how often examiners will find the true match among the top-ranked candidates.

Article Topics

biometrics  |  ELFT  |  facial recognition  |  fingerprint biometrics  |  license plate readers  |  Neurotechnology  |  SentiVeillance  |  video analytics  |  video surveillance